Correction:aBIOTECH https://doi.org/10.1007/s42994-025-00245-0 In this article the author“Yongqing Suo”should read“Yongqiang Suo”.The original article has been corrected.Open Access This article is licensed under ...Correction:aBIOTECH https://doi.org/10.1007/s42994-025-00245-0 In this article the author“Yongqing Suo”should read“Yongqiang Suo”.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.展开更多
Fusarium Head Blight(FHB),a fungal wheat(Triticum aestivum)disease that threatens global food security,requires precise quantification of diseased spikelet rate(DSR)as a phenotypic indicator for resistance breeding.Mo...Fusarium Head Blight(FHB),a fungal wheat(Triticum aestivum)disease that threatens global food security,requires precise quantification of diseased spikelet rate(DSR)as a phenotypic indicator for resistance breeding.Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting,which is inefficient and destructive.Although deep learning offers great promise for automated DSR measurement,existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data,insufficient feature representation for diseased spikelets,and weak spatial encoding of densely arranged spikelets.To address these challenges,we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field.We designed FHBDSR-Net,a light framework for automated DSR measurement centered on diseased spikelet detection,which features(1)multi-scale feature enhancement architecture that dynamically combines lesion textures,morphological features,and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise;(2)the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts;and(3)a scale-aware attention module using dilated convolutions and selfattention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution.FHBDSR-Net detected diseased spikelets with an average precision of 93.8%with a lightweight design of 7.2 M parameters.The results were strongly correlated with expert evaluations,with a Pearson correlation coefficient of 0.901.Our method is suitable for deployment on resourceconstrained mobile devices,facilitating portable plant phenotyping and smart breeding.展开更多
文摘Correction:aBIOTECH https://doi.org/10.1007/s42994-025-00245-0 In this article the author“Yongqing Suo”should read“Yongqiang Suo”.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.
基金supported by the National Natural Science Foundation of China(grant nos.32200331 and U24A20344).
文摘Fusarium Head Blight(FHB),a fungal wheat(Triticum aestivum)disease that threatens global food security,requires precise quantification of diseased spikelet rate(DSR)as a phenotypic indicator for resistance breeding.Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting,which is inefficient and destructive.Although deep learning offers great promise for automated DSR measurement,existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data,insufficient feature representation for diseased spikelets,and weak spatial encoding of densely arranged spikelets.To address these challenges,we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field.We designed FHBDSR-Net,a light framework for automated DSR measurement centered on diseased spikelet detection,which features(1)multi-scale feature enhancement architecture that dynamically combines lesion textures,morphological features,and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise;(2)the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts;and(3)a scale-aware attention module using dilated convolutions and selfattention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution.FHBDSR-Net detected diseased spikelets with an average precision of 93.8%with a lightweight design of 7.2 M parameters.The results were strongly correlated with expert evaluations,with a Pearson correlation coefficient of 0.901.Our method is suitable for deployment on resourceconstrained mobile devices,facilitating portable plant phenotyping and smart breeding.
